1. Building a graph database requires modeling the data, choosing a query language, and providing storage. 2. Existing distributed databases like Cassandra can be used for storage due to their scalability and reliability, though a native graph database provides more functionality. 3. Solving complex graph problems requires capabilities beyond basic queries, including search, analytics, and integration with machine learning, which graph databases are designed to support at scale.

1. How to build a
scalable graph database
Bryn Cooke
The smart way

2. In this talk
1. What does it take to build a graph database?
2. Why shouldn’t you do this at home.
3. What do you use this for?

3. Graph family tree

4. Graph database recipe
1. Model
2. Language
3. Storage

5. Model
bob
since: 2001
steph
bob:
knows
:steph
age: 30age: 34
knows known
Property Graph RDF

6. Language
g.V().has('name', 'marko').out('knows').values('name')

7. Storage

8. The adjacency list
Vertex Adjacent to
A B, D, E
B
C B
D C
E D, F
F
A
B
C
E
D
F

9. //TODO
• Storage
• Indexing
• Commit log
• Drivers
• Caching
• Schema
• Metrics
• Backup/Restore
• Logging
• Security
• Testing
• Support
• Failover
• QoS
• Paging
• Partitioning
• Sorting
• Compaction
• Repair
• Community
• Bux fixing
• Optimisation

10. Storage - Cassandra
• Fast
• Distributed
• Scalable
• Reliable
• 11 years of development
• 54 committers (listed on apache)
• 274 contributors (listed on github)

11. The adjacency list (in Cassandra)

12. Here's what you could do
C*
C*
C*
C*C*
My Graph
Database
Client
Client
Client
Client

13. Client
Here's what you could do
C*
C*
C*
C*C*
My Graph
Database

14. Here's what you should do
C*
C*
C*
C*C*
DS Graph
Client
Client
Client
Client

15. Deep integration with DataStax Enterprise
DataStax Enterprise
• DataStax Enterprise scalability > Cassandra scalability.
• Analytics integration.
• Search integration.
• Thread optimisation.
• Continuous paging.
• Prefetching.
• First class schema integration.

16. Today’s Graph Database Market
Graph
Problems > Graph
Databases

17. Typical customer 360 queries
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
• Find me Jenny.
• Find me all people
with similar names
to 'Jenny'.
• Tell there are
duplicate Jennys.
• Find how Jenny
and John are
connected.
• Find how
influential Jenny is
in my application.

18. Find me Jenny
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Simple
How Fast?
• Machine
What?
• CQL
Why?
• Single partition
lookup
• Single iteration

19. Find me all people with similar names to 'Jenny'
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Medium
How Fast?
• Human Fast
What?
• Search
• Graph
Why?
• Single index
lookup
• Single iteration

20. Tell there are duplicate Jennys
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Medium
How Fast?
• Offline
What?
• Analytics
• Graph
Why?
• Aggregation
• Multiple Iteration

21. Find how Jenny and John are connected
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Complex
How Fast?
• Machine
What?
• Graph
Why?
• Multiple partition
lookup
• Multiple iteration

22. Find how influential Jenny is in my application
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Complex
How Fast?
• Offline
What?
• Spark Analytics
• Graph via PageRank
Why?
• Full scan
• Unknown iterations

23. Typical customer 360 queries
Offline
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
• Find me Jenny.
• Find me all people
with similar names
to 'Jenny'.
• Tell there are
duplicate Jennys.
• Find how Jenny
and John are
connected.
• Find how
influential Jenny is
in my application.

24. Summary
1. What it takes to create a graph database
a. Model
b. Language
c. Storage
2. How you can leverage an existing storage engine, and why Cassandra is a
great choice.
3. Solving graph problems requires more than just the basics. Search and
Analytics are essential tools, especially graph database.

25. Don't try this at home
Do not try replicate 100 person years of
dev effort creating your own storage
engine.
Creating a graph database that scales is
tough enough.

26. Try it now
https://downloads.datastax.com/#labs
Labs

27. Thank You